Acute COVID-19 infection frequently affects the cardiovascular system and causes acute myocardial injury. Epicardial Adipose Tissue (EAT), a visceral adipose tissue surrounding the myocardium and coronary arteries, has unique paracrine and endocrine effects, modulating the heart's inflammatory environment. Systemic inflammation stimulates TNF-α and Interleukin-6 secretion from EAT, contributing to cytokine storms and intensifying systemic responses. We aimed to determine whether EAT amount differs in patients with and without acute COVID-19 infection and myocardial injury.

This study analyzed the CoV-COR registry cohort, conducted at the University Hospital Essen, including patients with symptoms suggestive of COVID-19 infection. The infection was confirmed by PCR. EAT thickness was measured by two-dimensional TTE.

A total of 296 patients (mean age 63.6 ± 17.26 years, 55.4 % male) were included. Patients with confirmed COVID-19 infection were younger, more frequently treated with antihypertensive medication, and had higher BMI and systolic blood pressures. Univariate logistic regression showed no association between EAT and myocardial injury 0.97 (0.74; 1.28, p = 0.82). A trend towards an association was observed between increasing EAT thickness and COVID-19 infection 1.25 (0.99; 1.59, p = 0.060). Adjusting for age and gender strengthened the association, with a 48 % (1.14; 1.93, p = 0.004) increased odds of COVID-19 infection per increase in EAT thickness. Multivariable regression yielded consistent effect sizes 1.47 (1.01; 2.16, p = 0.047).

EAT thickness is associated with the presence of an acute COVID-19 infection but not with a myocardial injury. Further research is needed to assess if systemic viral infection induces dynamic changes in EAT.

To describe clinical-epidemiological and therapeutic aspects and to estimate case fatality ratio and risk factors for lower in-hospital survival due to COVID-19.

This is a retrospective cohort study conducted in the state of Goiás, Brazil, in 2020, with data obtained from the Influenza Epidemiological Surveillance Information System and through a review of clinical records and hospital. Relative risk for in-hospital death was estimated and Poisson multiple regression and Cox regression analyses were performed. Survival functions were compared using the log-rank test and represented by Kaplan-Meier curves.

The sample consisted of 651 adults, whose median age was 59 years, 57.0% were admitted to public hospitals, 61.1% had severe acute respiratory syndrome on admission and 72.0% had at least one comorbidity, the most frequent being hypertension , diabetes and obesity. The overall case fatality ratio was 17.5% (95% confidence interval, 95%CI 14.7; 20.6), with no significant difference between public and private hospitals. The case fatality ratio was higher in the ≥60 years age group (prevalence ratio, PR 1.26; 95%CI 1.01; 1.58), in hypertensive patients (PR 1.41; 95%CI 1.14; 1 .74) and in those undergoing intensive care (PR 2.68; 95%CI 1.13; 6.32) and mechanical ventilation (PR 11.15; 95%CI 5.53; 22.46). The median time between hospital admission and death was 10 days (interquartile range, 6-18). Survival was lower in the ≥60 years age group (adjusted hazard ratio, HR 1.93; 95%CI 1.26; 2.95) and in those undergoing mechanical ventilation (HR 10.13; 95%CI 6.03; 17. 02).

Factors related to comorbidities and severity were independent predictors of shorter in-hospital survival among patients with COVID-19.

Life expectancy in high-income countries remained lower in 2022 compared to pre-pandemic levels in 2019. This study explores the deficit of life expectancy and excess of years of life lost (YLL) in Spain from 2020 to 2022, assessing both direct effects of infectious diseases and indirect effects of other causes of death.

Data on life expectancy and YLL from 2010 to 2022 were obtained from the Spanish Institute for Statistics (INE). Using linear regression, we estimated expected life expectancy and YLL for 2020-2022 under the assumption that pre-pandemic trends (2010-2019) had continued.

During the first year of the pandemic, Spanish women lost 1.10 years and men lost 1.40 years in life expectancy. By 2022, life expectancy remained lower than in 2019 for both sexes. The excess YLL was similar across 2020 (2.40 million YLL and 5.3 YLL/100 people), 2021 (2.35 million YLL, 5.1 YLL/100 people), and 2022 (2.35 million YLL, 5.0 YLL/100 people). Approximately 70% of this excess was attributable to infectious diseases (87% in 2020, 78% in 2021, and 43% in 2022). Other major contributors to excess YLL included external causes, circulatory diseases, digestive diseases, and endocrine, nutritional, and metabolic diseases, while cancer mortality did not show an excess during the pandemic period.

Mortality in Spain in 2022 remained elevated compared to pre-pandemic expectations. The contribution of non-infectious diseases to excess mortality increased over time.

Not applicable.

Females demonstrate elevated type-I interferon production and a stronger antiviral immune response; however, the mechanisms underlying sex-based differences in antiviral immunity are incompletely understood. We previously reported that low adenosine deaminase (ADA) activity perturbs the methylation-based transcriptional silencing of endogenous retroviral elements (hERV), which stimulates IFN-Stimulated Genes (ISG) and primes antiviral immunity. Here we demonstrate lower ADA activity in females compared to their male counterparts, which correlated with higher hERV and ISG expression in female lungs. Sex differences in ADA2 were linked to the number and expression profiles of blood and lung-derived monocyte populations. Single-cell RNA sequencing of respiratory cells from patients with COVID-19 showed a significant female bias in hERV-ISG signatures, and implicated IL-18 as a driver of sex-specific ADA2 expression. Observations in healthy and COVID-19 cohorts indicate that higher ADA activity is associated with suppressed antiviral innate immunity in the male respiratory tract, which may drive adverse COVID-19 outcomes.

Many questions remain unanswered regarding the implication of genetics and lipid metabolites with severe SARS-CoV-2 infections. We performed bulk RNA-seq and a total fatty acid panel analysis on PBMCs and plasma collected from 10 infected and 10 uninfected patients. Univariate comparison of lipid metabolites using the Mann-Whitney U-test revealed that six lipid metabolites were significantly increased in COVID-19 patients, including the lipid mediators arachidonic acid (AA) and eicosapentaenoic acid (EPA), which both give rise to eicosanoids. Key lipids implicated in inflammation, including AA and EPA, along with the fatty acids DHA and DPA, were significantly and positively correlated to the WHO disease severity score. Analysis of our bulk RNA-seq dataset demonstrated distinct transcriptional profiles leading to a segregation of COVID-19 patients based on the WHO score. Ontology, KEGG, and Reactome analysis identified several key pathways and nodes that were enriched for genes related to innate immunity, interactions between lymphoid and nonlymphoid cells, interleukin signaling, and subsequent DNA damage pathways. EPA levels correlated with heightened cell cycling and DNA damage pathways observed in patients with a high WHO score. We studied gene expression in nasopharyngeal swabs from 58 healthy and COVID-19 participants and identified that genes implicated in eicosanoid synthesis, such as alox5, alox12, and alox15B, were specifically up-regulated in high WHO score patients in several cell types of the nasopharynx, especially goblet cells across different viral variants (Deta and Omicron). Using published nasal scRNA-seq datasets from COVID-19 patients, we evaluated the expression of genes implicated in eicosanoid synthesis, such as ALOX5, ALOX15, and ALOX15B, across nasal cell types and COVID-19 severity groups. Altogether, our study highlights the fact that the increase in specific lipids implicated in inflammation and the genes required for their synthesis correlated with the severity of the SARS-CoV-2 infection.

COVID-19 disease has been a deadly pandemic in different waves in the Democratic Republic of Congo. However, knowledge of the clinical characteristics of COVID-19 patients and the factors associated with death during different waves is important.

We conducted a retrospective cohort of 410 patients hospitalized during 4 waves of COVID-19, from March 20, 2020, to January 2, 2022, at the Ngaliema clinic in DR Congo. We included any patient hospitalized for COVID-19 with biological confirmation by RT-PCR. Factors associated with death were investigated using logistic regression.

During the 4 waves of the COVID-19 pandemic at Clinique Ngaliema, complaints on admission were most often fever, cough and physical asthenia. Death was most common in the elderly, hypertensive and diabetic patients, those with elevated CRP and hyper leukocytosis. Mortality was highest in the 1st wave (28%), followed by the 3rd wave (27%), then the 2nd (22%) and 4th waves (21%). Factors associated with death were hyper leukocytosis (ORa: 2.76; CI 95%: 1.25-6.1), severe disease stage (ORa 21.24; CI 95%: 1.87-24). Vitamin C 500 mg twice a day use was protective (ORa: 0.24; CI 95%: 0.08-0.72).

COVID-19 disease poses a real public health problem, with non-negligible mortality. Factors associated with death were degree of disease severity, hyper leukocytosis and non-use of vitamin C. Taking these factors into account will help clinicians and decision-makers to anticipate future waves of the pandemic.

Few studies have captured the impact of inadequate nurse staffing levels and broader health patient conditions in admitted patients during the COVID-19 pandemic. We aimed to determine the association between nurse staffing coverage, care complexity individual factors (CCIFs) and adverse events (AEs) in patients admitted with COVID-19.

A multicentre cross-sectional study was conducted from March 1, 2020 to March 31, 2022 at eight public health hospitals in Spain. All patients with COVID-19 who were admitted to these hospitals were included. The main variables included AEs, nurse staffing coverage (as measured using the ATIC patient classification system) and CCIFs to evaluate broader patient health conditions. Adjusted logistic models were performed to identify associations with AEs, stratified by patients admitted to wards and hospitalized patients who required admission to intensive care units (ICUs).

A total of 11,968 hospitalized patients, 2,824 (23.6%) experienced AEs. Multivariate analysis showed that higher levels of nurse staffing coverage protected against AEs. Among patients admitted to acute wards, the independent risk factors for AEs included old age, haemodynamic instability, chronic disease, uncontrolled pain, urinary or faecal incontinence and mental status impairments. In addition to these factors, extreme weight, position impairment and communication disorders were factors associated with AEs in patients who required ICU admission.

Nurse staffing coverage was a protective factor for AEs. Several CCIFs related to comorbidity/complications, developmental, and mental-cognitive domains were strongly associated with AEs. Therefore, ensuring safe nurse staffing levels could be improve patient outcomes.

The study aimed to compare the sociodemographic, comorbidity, and clinical variables associated with coronavirus disease 2019 (COVID-19) mortality across three distinct epidemic waves in Faridabad, India.

A retrospective analysis of the medical records of patients admitted with COVID-19 was conducted at a tertiary care center at Faridabad, India. COVID-19 epidemic waves were categorized into the first wave (April 2020-January 2021), second wave (March 2021-June 2021), and third wave (December 2021-February 2022). Sociodemographic, comorbidity, and clinical parameters were assessed for their association with mortality in each of the waves by the Chi-square test. The Cochran-Armitage test for trend was used to assess changes in these associations with respect to the mortality rate across the epidemic waves.

A total of 5217 patient records were assessed, with 4066 in the first wave, 895 in the second wave, and 256 in the third wave. Across all waves, comorbidities (diabetes and hypertension), multimorbidity, severe disease (requiring intensive care unit admission and ventilator support) were consistently associated (p<0.05) with higher mortality. While sociodemographic factors were significant (p<0.05) in the first two waves, their impact diminished in the third. Clinical symptoms, particularly 'cold and flu' showed consistent significance (p<0.05) across all waves. COVID-19 mortality trend peaked in the second wave, disproportionately (p<0.05) affecting females, older patients, and those with comorbidities or severe symptoms.

Understanding the shifting risk factors across COVID-19 epidemic waves is crucial for targeted interventions. Prioritizing high-risk groups, particularly during peak waves, can optimize resource allocation and minimize mortality.

Despite global efforts to mitigate COVID-19 infection through vaccination and therapeutic interventions, morbidity and mortality rates continued at variable rates. Although mortality risk and clinical features of COVID-19 are well-documented, recovery patterns and prognostic factors post-admission remain inconclusive, particularly in resource-limited settings like Ethiopia. This systematic review and meta-analysis (SRM) aimed to estimate the pooled incidence rate of recovery and predictors among hospitalized COVID-19 patients in Ethiopia.

We searched (N = 1,191) articles using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline from PubMed/MEDLINE (N = 755), Scopus (N = 137), Web of Science (N = 84), Science Direct (N = 148), Cochran (N = 25), and Google Scholar searching (N = 42) from December 2019 to February 2024. The data were extracted using a Microsoft Excel spreadsheet and exported to Stata TM version 17.0 for further analysis. The Article quality was assessed using the Joanna Briggs Institute checklist. The pooled incidence rate of recovery was estimated using a weighted inverse variance random-effects meta-regression. Heterogeneity among studies was evaluated using the I2 statistic. Subgroup analyses and sensitivity tests were also conducted to explore publication bias. This file is registered in international Prospero with ID (CRD42024518569).

Sixteen (N = 16) published studies with 7,676 hospitalized COVID-19 patients were included in the final report. The mean age of participants ranged from 29 (± 17) to 57.5 (± 3) years, with male patients constituting the largest proportion of participants, 4,491(58.5%). During recovery screening, 6,304(82.21%) cases were discharged as improved, 159 (2.1%) attriters, and 818 (10.6%) died during inpatient treatment. The pooled incidence of recovery, mortality, and attrition rates were found to be 82.32% (95% CI: 78.81-85.83; I2 = 94.8%), 14.3% (I2 = 98.45%), and 2.7% (I2 = 81.34%), respectively. Incidence of recovery rate varied across regions and epidemic phases, with the highest rate observed in Addis Ababa (89.94%, I2 = 78.33%) and the lowest reported in the Tigray region (59.7%, I2 = 0.0%). Across epidemic phases, the recovery rate was 88.05% (I2 = 29.56%) in Phase II, 84.09% (I2 = 97.57%) in Phase I, and 78.92% (I2 = 96.9%) in Phase III, respectively. Factors included being aged 15-30 years (pooled OR = 2.01), male sex (pooled OR = 1.46), no dyspnea (pooled OR = 2.4; I2 = 79%), and no baseline comorbidities (pooled OR = 1.15; I2 = 89.3%) were predictors for recovery. CONCLUSION AND RECOMMENDATION: In Ethiopia, more than eight out of ten hospitalized COVID-19 patients recovered after inpatient treatment. However, the incidence of recovery rates varied significantly across epidemic phases, study settings, and regions. Factors including younger age, male sex, no dyspnea (shortness of breathing), and no underlying comorbidity heightened recovery. It is highly recommended those inpatients cares should focus on high-risk groups (older adults) and implement standardized treatment protocols in each study setting. Regions with lower recovery rates need aid in logistical support and training for healthcare providers.

Diabetes mellitus (DM) and hyperglycemia are associated with poor outcome(s) in COVID-19 hospitalized patients, but their independent impact on prognosis remains unclear. We aimed to assess the impact of DM and hyperglycemia on COVID-19 outcomes.

Clinical data/records from COVID-19 patients admitted to the Parma University-Hospital (February 23rd to March 31st, 2020) were retrieved and analysed (NCT04550403). Fasting plasma glucose (FPG), inflammatory markers and the main biochemical variables were collected at admission. Patients underwent chest high-resolution CT and arterial blood gas analysis to determine the PaO2/FiO2 ratio (P/F ratio). The primary outcome was a composite of intensive care unit admission and/or death.

Among 756 subjects, 143 (19%) had DM. These patients were older with higher comorbidity rates. The primary outcome occurred in 61.5% DM patients versus 43.4% without DM (p < 0.001). In multivariable analysis (accuracy UC = 0.93), older age, cardiovascular and kidney diseases, FPG ≥ 126 mg/dl, C-reactive protein, and P/F ratio, but not previous DM, were independent risk indicators.

DM indicated poor COVID-19 outcomes, but not when adjusted for other clinical variables/comorbities, suggesting that its impact was mostly driven by concomitant factors. The independent role of fasting hyperglycemia points to the need for further research on its contribution to COVID-19.

The prognosis of patients with acute myocardial infarction (AMI) in the setting of coronavirus disease 2019 (COVID-19) remains uncertain.

To evaluate patients' prognosis after an AMI concomitant with COVID-19.

This retrospective nationwide observational cohort study was based on the French administrative hospital discharge database. Primary outcomes were incidences of all-cause death, cardiovascular death, heart failure (HF), recurrence of AMI, ischaemic stroke, incident atrial fibrillation (AF), ventricular tachycardia/ventricular fibrillation (VT/VF) and cardiac arrest. Patients with AMI and COVID-19 were matched to those without COVID-19 (using propensity score matching techniques) to account for differences between the two populations.

A total of 288,408 patients hospitalized for AMI in France from March 2020 to January 2023 were included; 26,879 had a COVID-19-positive test between 15 days before to 5 days after admission. Patients with COVID-19 were older, more frequently had diabetes mellitus and obesity but less frequently smoked. They more frequently had non-ST-segment elevation myocardial infarction presentation and more often had lung disease. After matching, patients with COVID-19 had higher risks of all-cause death (hazard ratio [HR] 1.255; 95% confidence interval [CI] 1.203-1.308; P<0.0001), HF (HR 1.205; 95% CI 1.159-1.254; P<0.0001), ischaemic stroke (HR 1.237; 95% CI 1.084-1.411; P=0.002), incident AF (HR 1.160; 95% CI 1.070-1.258; P=0.0003) and VT/VF (1.360; 95% CI 1.200-1.540; P<0.0001). Surprisingly, cardiovascular death risk was lower in patients with COVID-19 (HR 0.932; 95% CI 0.879-0.988; P=0.02) as a result of competition with non-cardiovascular death. No statistical difference was found for cardiac arrest or recurrent AMI.

In this French nationwide cohort study, AMI in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) increased all-cause death incidence compared to non-infected AMI, but this poorer prognosis was not due to cardiovascular death. Further investigations are needed to elucidate the aetiologies of death.

Although, COVID-19 has resulted in over 7 million deaths globally, many questions still remain about the risk factors for disease severity and the effects of variants and vaccinations over the course of the pandemic. To address this gap, we conducted a retrospective analysis of electronic health records from COVID-19 patients over 2.5 years of the COVID-19 pandemic to identify associated clinical features.

We analyze a retrospective cohort of 21,312 acute-care patients over a 2.5 year period and define six clinical trajectory groups (TGs) associated with demographics, diagnoses, vitals, labs, imaging, consultations, and medications.

We show that the proportion of mild patients increased over time, particularly during Omicron waves. Additionally, while mild and fatal patients had differences in age, age did not distinguish patients with severe versus critical disease. Furthermore, we find that both male sex and Hispanic/Latino ethnicity are associated with more severe/critical TGs. More severe patients also have a higher rate of neuropsychiatric diagnoses and consultations, along with an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin is one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (e.g. Vitamin D, thiamine, B vitamins) or received vitamin supplementation.

Our findings expand on clinical risk factors in COVID-19, and highlight the interaction between severity, nutritional status, and neuropsychiatric complications in acute care patients to enable identification of patients at risk for severe disease.

Background: Artificial intelligence tools can help improve the clinical management of patients with severe COVID-19. The aim of this study was to validate a machine learning model to predict admission to the Intensive Care Unit (ICU) in individuals with COVID-19. Methods: A total of 201 hospitalized patients with COVID-19 were included. Sociodemographic and clinical data as well as laboratory biomarker results were obtained from medical records and the clinical laboratory information system. Three machine learning models were generated, trained, and internally validated: logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). The models were evaluated for sensitivity (Sn), specificity (Sp), area under the curve (AUC), precision (P), SHapley Additive exPlanation (SHAP) values, and the clinical utility of predictive models using decision curve analysis (DCA). Results: The predictive model included the following variables: type 2 diabetes mellitus (T2DM), obesity, absolute neutrophil and basophil counts, the neutrophil-to-lymphocyte ratio (NLR), and D-dimer levels on the day of hospital admission. LR showed an Sn of 0.67, Sp of 0.65, AUC of 0.74, and P of 0.66. RF achieved an Sn of 0.87, Sp of 0.83, AUC of 0.96, and P of 0.85. XGBoost demonstrated an Sn of 0.87, Sp of 0.85, AUC of 0.95, and P of 0.86. Conclusions: Among the evaluated models, XGBoost showed robust predictive performance (Sn = 0.87, Sp = 0.85, AUC = 0.95, P = 0.86) and a favorable net clinical benefit in the decision curve analysis, confirming its suitability for predicting ICU admission in COVID-19 and aiding clinical decision-making.

We examined the association between the COVID-19-specific prefectural bed utilization rate and in-hospital mortality during the first three years of the pandemic in Japan.

This nationwide study included 58,175 COVID-19 patients from the COVID-19 Registry Japan, hospitalized between May 1, 2020 and November 30, 2022. Based on the weekly COVID-19-specific bed utilization rate in each prefecture at diagnosis, patients were categorized into four groups (< 25%, 25% to < 50%, 50% to < 75%, and ≥ 75%). Odds ratios (ORs) were estimated by fitting a generalized linear mixed model with prefecture as a random intercept and adjusting for covariates (age, gender, body mass index, smoking and drinking status, and comorbidities). Additional analyses according to age group, gender, and wave of the pandemic were conducted.

We observed 2312 (4.0%) all-cause in-hospital deaths. All-cause in-hospital mortality increased with higher COVID-19 bed utilization rates at diagnosis (OR for multivariable model 1.35, 95% confidence interval [CI] 1.19-1.54 for 25% to <50%; 1.89, 1.66-2.16 for 50 to <75%; 2.16, 1.80-2.58 for ≥75%; P for trend<0.0001). Stronger associations were noted among the younger population (aged <70 years, OR: 3.18, 1.96-5.19) and during the fourth (March 1-June 30, 2021, OR: 3.81, 2.13-6.80) and sixth pandemic waves (January 1-Jun 30, 2022, OR: 2.67, 1.68-4.23).

Our results emphasize that preventing hospital bed shortages during outbreaks is an important public health strategy to reduce the associated mortality, particularly when new strains emerge and in younger people.

Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. This study aims to evaluate factors related to patients and hospitals in the prediction of in-hospital mortality, need for invasive mechanical ventilation (IMV), and intensive care unit (ICU) transfer throughout the pandemic waves.

This multicenter retrospective cohort included COVID-19 patients from 39 hospitals, from March/2020 to August/2022. The pandemic was divided into waves: 10/03/2020-14/11/2020 (first), 15/11/2020-25/12/2021 (second), 26/12/2021-03/08/2022 (third). Patient-related factors included clinical, demographic, and laboratory data, while hospital-related factors covered funding sources, accreditation, academic status, and socioeconomic characteristics. Shapley additive explanation (SHAP) values derived from the predictions of a light gradient-boosting machine (LightGBM) model were used to assess potential risk factors for death, IMV and ICU.

Overall, 16,958 adult patients were included (median age 59 years, 54.7% men). LightGBM achieved competitive effectiveness metrics across all periods. Temporal drifts were observed due to a decrease in various metrics, such as the recall for the positive class [ICU: 0.4211 (wave 1) to 0.1951 (wave 3); IMV: 0.2089 (wave 1) to 0.0438 (wave 3); death: 0.2711 (wave 1) to 0.1175 (wave 3)]. Peripheral arterial oxygen saturation to the fraction of inspired oxygen ratio (SatO2/FiO2) at admission had great predictive capacity for all outcomes, with an optimal cut-off value for death prediction of 227.78. Lymphopenia had its association strength increased over time for all outcomes, optimal threshold for death prediction of 643 × 109/L. Thrombocytopenia was the most important feature in wave 2 (ICU); overall, values below 143,000 × 109/L were more related to death.

Data drifts were observed in all scenarios, affecting potential predictive capabilities of explainable machine learning methods. Upon admission, SatO2/FiO2 values, platelet and lymphocyte count were significant predictors of adverse outcomes in COVID-19 patients. Overall, inflammatory response markers were more important than clinical characteristics. Limitations included sample representativeness and confounding factors. Integrating the drift's knowledge into models to improve effectiveness is a challenge, requiring continuous updates and monitoring of performance in real-world applications.

Not applicable.

Patients with systemic autoimmune rheumatic disease (SARD) are a vulnerable population for severe COVID-19 and worse response to vaccination, prompting the need of a booster vaccine. Data regarding its response is limited and inconsistent. The aim of this study was to assess the effectiveness and immunogenicity of the third dose of the SARS-CoV-2 vaccine in immunosuppressed SARD patients.

We conducted a prospective study in immunosuppressed SARD Portuguese patients, who received a SARS-CoV-2 booster vaccine, from October 2021 to August 2022. We evaluated COVID-19 incidence in the following 6 months, as well as vaccine immunogenicity through anti-Spike IgG titers and T-cell reactivity to the Spike protein.

We included 131 patients with a mean age of 54.9±12.2 years. Almost 40% (n=52) developed COVID-19 within 6 months after the booster, but 51 (98.1%) were mild infections. Median post-booster antibody levels and antibody variation were 9540.7 (14,724) and 8937.9 (11,561.3)AU/mL, respectively, and 73.3% (n=96) of the patients showed post-booster T-cell reactivity. Antibody variation was significantly lower in the COVID group (p=0.015). Although post-booster antibody levels and T-cell reactivity were statistically significantly lower in the patients under biologic DMARD, there was not a significant increase in COVID-19 incidence.

This study shows that a booster vaccine elicits strong immunogenicity and reduces COVID-19 severity, highlighting its importance in immunosuppressed SARD patients. Larger and more homogeneous cohorts are needed to guide periodic booster administration in this susceptible population.

To describe and explain the effect of the coronavirus disease 2019 pandemic and its related measures on return to work (RTW) outcomes after multimodal medical rehabilitation for musculoskeletal disorders.

Retrospective cohort study.

Three cohorts: reference (rehabilitation and RTW prepandemic), pandemic 1 (rehabilitation prepandemic, RTW during pandemic), and pandemic 2 (rehabilitation and RTW during pandemic).

Individuals who underwent multimodal medical rehabilitation for musculoskeletal disorders through the German Pension Insurance system between January 2018 and December 2021 (N=688,127).

Not applicable.

A successful and stable RTW was operationalized as having employment subject to social insurance contributions from 9 to 12 months after rehabilitation.

Descriptive analysis revealed an RTW rate of 67.2% in the reference cohort, a slight decline in pandemic cohort 1 (66.3%), and a more pronounced decrease in pandemic cohort 2 (63.1%). In contrast, average marginal predictions from a logistic model including various covariates showed that both pandemic cohorts (63.8% and 64.4%) exhibited similarly reduced predicted probabilities of RTW compared with the reference cohort (66.5%). Individuals with sick leave durations exceeding 6 months, compared with those with shorter sick leaves, were more negatively affected by pandemic cohort 1; however, this effect recovered in pandemic cohort 2. The interaction between cohort and income did not show any amplifying effect of the pandemic.

The logistic model revealed no differences in predicted probabilities of RTW between pandemic cohort 2 and pandemic cohort 1, suggesting that orthopedic rehabilitation remained robust in maintaining RTW outcomes despite pandemic-related challenges. The findings offer mixed evidence regarding the question of whether the pandemic amplified preexisting barriers to RTW.

To assess the impact of severe COVID-19 in patients with immune-mediated rheumatic diseases (im-RD) and compare their morbidity, mortality, hospitalization issues, post-COVID-19 sequelae, and the financial burden of COVID-19 with those of patients without im-RD.

We conducted a retrospective case-control study that included 132 consecutive patients with im-RD who visited the Rheumatology Department of a public hospital in the Emirate of Dubai and were hospitalized for COVID-19 infection between March 1st, 2020, and December 31st, 2021, (cases). We included 264 and 132 age- and sex-matched patients without im-RD in matched-I and matched-II control groups, respectively. The median age of patients and controls was 48.5 years, and 74.2% were female. Patients with im-RD were paired with an unforced nearest neighbor match using a caliper width of 0.2 standard deviations of the matched-II control group's propensity score. We compared the relative risk of death, disease progress, use of medical resources, and financial impact of COVID-19 between patients and controls.

Patients with im-RD had higher mortality rates than the matched-I (odds ratio, OR: 11.2, p < 0.000) and matched-II (OR: 16.8, p < 0.006) control groups. The overall complication rate was also significantly higher in patients with im-RD than in matched-I (OR = 2.9, p < 0.000) and matched-II (OR = 2.8, P < 0.0001) control groups. Lastly, patients with im-RD required more frequent visits to the clinic, a longer recovery time following hospital discharge, and increased healthcare costs compared to the control groups.

COVID-19 infection in patients with im-RD is associated with increased morbidity and mortality, exerting a significant burden on the healthcare system.

Obstructive sleep apnea and sleep-related hypoxia have been associated with higher rates of hospitalization and mortality among patients with Coronavirus disease 2019 (COVID-19). We further explored the association between obstructive sleep apnea, COVID-19 severity and related mortality. In addition, we examined the effects of clinical and demographic parameters on COVID-19. In this retrospective study, we included adult patients who were diagnosed with COVID-19 prior to the Omicron variant identification. We compared the severity of COVID-19 and mortality with the diagnosis of obstructive sleep apnea. The study population included 44,275 patients who tested positive for COVID-19. Of these, 97% had mild or asymptomatic disease, 1.2% had moderate disease, and 1.8% had severe disease. Obstructive sleep apnea was diagnosed in 980 (2.2%) patients. In a multivariate analysis, obstructive sleep apnea diagnosis increased the risk of severe COVID-19 by 1.6 (95% confidence interval: 1.1-2.4) compared with mild disease. However, no increase in mortality was associated with obstructive sleep apnea. Interestingly, patients with moderate and high socioeconomic status had a 1.6 times higher risk for severe COVID-19 than patients from the low socioeconomic status group (95% confidence interval: 1.2-2.1 and 95% confidence interval: 1.1-2.3, respectively). The risk of dying due to COVID-19 was 1.6 (95% confidence interval: 1.1-2.5) and 3.1 (95% confidence interval: 1.8-5.3) times higher in patients with medium and high socioeconomic status, respectively, compared with patients with low socioeconomic status. Diagnosis of obstructive sleep apnea was found to be an independent risk factor for severe COVID-19. The higher the socioeconomic status, the higher the risk of severe COVID-19 morbidity and mortality.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 and long COVID can present with nonspecific symptoms resembling adrenal insufficiency. This similarity of symptoms means that adrenal insufficiency hidden among nonspecific manifestations of COVID-19 may pass underrecognized. We present the case of a 53-year-old Japanese man who developed isolated adrenocorticotrophic hormone (ACTH) deficiency (IAD) and acute adrenal insufficiency after COVID-19, thus mimicking prolonged symptoms of COVID-19. The patient developed fever, cough, and sore throat and was diagnosed with COVID-19. After anti-viral treatment, fever, loss of appetite, and general fatigue persisted, and hyponatremia was observed. Endocrinological testing on admission showed baseline concentrations of 3.5 μg/dL for cortisol and <1.5 pg/mL for ACTH. No abnormalities of other pituitary hormones were evident. Standard ACTH stimulation tests showed a decreased peak serum cortisol concentration and corticotropin-releasing hormone stimulation tests revealed no ACTH secretory response. Magnetic resonance imaging of the pituitary revealed no abnormalities. Adrenal insufficiency due to IAD was diagnosed based on the results of endocrinological testing. Intravenous and oral hydrocortisone improved symptoms and hyponatremia. The patient has experienced no recurrence of adrenal insufficiency under hydrocortisone treatment. COVID-19 can mimic adrenal insufficiency. IAD should be considered when nonspecific symptoms persist after treatment for COVID-19.

The entry and infection of the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2) involve recognition and binding of the receptor-binding domain (RBD) of the virus surface spike protein to the peptidase domain (PD) of the host cellular Angiotensin-Converting Enzyme-2 (ACE2) receptor. ACE2 is also involved in normal blood pressure control. An association between hypertension and COVID-19 severity and fatality is evident, but how hypertension predisposes patients diagnosed with COVID-19 to unfavorable outcomes remains unclear. High temperature early during SARS-CoV-2 infection impairs binding to human cells and retards viral progression. Low body temperature can prelude poor prognosis. In this study, all-atom molecular dynamics simulations were performed to examine the effects of high pressure and temperature on RBD/PD binding. A high blood pressure of 940 mmHg enhanced RBD/PD binding. A high temperature above 315 K significantly weakened RBD/PD binding, while a low temperature of 305 K enhanced binding. The curvature of the PD α1-helix and proximity of the PD β3β4-hairpin tip to the RBM motif affected the compactness of the binding interface and, hence, binding affinity. These findings provide novel insights into the underlying mechanisms by which hypertension predisposes patients to unfavorable outcomes in COVID-19 and how an initial high temperature retards viral progression.

Many studies have investigated symptoms, comorbidities, demographic factors, and vaccine effects in relation to long COVID (LC-19) across global populations. However, a number of these studies have shortcomings, such as inadequate LC-19 categorisation, lack of sex disaggregation, or a narrow focus on certain risk factors like symptoms or comorbidities alone. We address these gaps by investigating the demographic factors, comorbidities, and symptoms present during the acute phase of primary COVID-19 infection among patients with LC-19 and comparing them to typical non-Long COVID-19 patients. Additionally, we assess the impact of COVID-19 vaccination on these patients. Drawing on data from the Regional Health System of the Region of Murcia in southeastern Spain, our analysis includes comprehensive information from clinical and hospitalisation records, symptoms, and vaccination details of over 675126 patients across 10 hospitals. We calculated age and sex-adjusted odds ratios (AOR) to identify protective and risk factors for LC-19. Our findings reveal distinct symptomatology, comorbidity patterns, and demographic characteristics among patients with LC-19 versus those with typical non-Long COVID-19. Factors such as age, female sex (AOR = 1.39, adjusted p < 0.001), and symptoms like chest pain (AOR > 1.55, adjusted p < 0.001) or hyposmia (AOR > 1.5, adjusted p < 0.001) significantly increase the risk of developing LC-19. However, vaccination demonstrates a strong protective effect, with vaccinated individuals having a markedly lower risk (AOR = 0.10, adjusted p < 0.001), highlighting the importance of vaccination in reducing LC-19 susceptibility. Interestingly, symptoms and comorbidities show no significant differences when disaggregated by type of LC-19 patient. Vaccination before infection is the most important factor and notably decreases the likelihood of long COVID. Particularly, mRNA vaccines offer more protection against developing LC-19 than viral vector-based vaccines (AOR = 0.48). Additionally, we have developed a model to predict LC-19 that incorporates all studied risk factors, achieving a balanced accuracy of 73% and ROC-AUC of 0.80. This model is available as a free online LC-19 calculator, accessible at ( LC-19 Calculator ).

Outbreaks of the novel respiratory viral disease, SARS-CoV-2 (COVID-19), have caused disproportionate morbidity and mortality for older people living in residential aged care homes. Between June 2021 and December 2022, the Delta and Omicron variants of COVID-19 were responsible for widespread outbreaks in homes across Western Sydney, New South Wales, Australia. To manage outbreaks in affected homes, a targeted response strategy was prepared and deployed in the form of outbreak management teams. This study utilised the Donabedian framework and a two-phase mixed methods design to evaluate the structures, processes and outcomes of the outbreak management teams at the level of the local health district. Phase 1 involved the descriptive analysis of outbreak data from Western Sydney aged care homes, created between June 2021 and December 2022. Phase 2 involved the completion of in-depth semi-structured interviews with 35 participants to explore the outbreak management team response from the perspective of its members and staff from residential aged care homes. Between June 2021 and December 2022, there were 281 outbreaks, 4113 resident cases, 346 hospitalisations and 127 deaths in residential aged care homes across Western Sydney. Structural factors that facilitated the outbreak management response and improved outcomes included smaller home sizes, the absence of shared rooms and bathrooms, adequate staffing and resources, suitable infrastructure, and the integration of the response with wider public health systems. Process facilitators included multi-disciplinary team membership, open communication channels, structured and streamlined procedures and roles, onsite infection control support and education, and long-term capability building. The lessons drawn from participants' experiences aim to improve the outcomes and sustainability of current and future outbreak management strategies.

We conducted a multicenter retrospective cohort study across 38 hospitals in Chiba, Japan, between February 1, 2020 and November 31, 2021 to investigate the effect of coronavirus disease 2019 (COVID-19) on patients with diabetes mellitus receiving inpatient care. We collected inpatient medical data through Diagnosis procedure combination (DPC), the diagnoses and payment system of medical insurance, from each hospital. We excluded patients younger than 18 years, those who were pregnant, and those who had diabetes but were not treated with diabetic medication. A total of 10,776 patients were included: 7,679 in the non-diabetic (control) group and 3,097 in the diabetic group. Patients in the diabetic group were older and had a higher body mass index (BMI) than those in the control group. In the diabetes group, 88.4% of the patients were treated with insulin therapy and 44.2% were treated with oral hypoglycemic agents. The length of hospital days was significantly longer in the diabetes group. The in-hospital mortality rate was significantly higher especially between 50 and 59 years old. The rates of in-hospital mortality, mechanical ventilation, intensive care unit (ICU) admission, renal replacement therapies such as hemodialysis (HD), and continuous hemodiafiltration (CHDF) were all higher, even after adjusting for age, sex, BMI, and ambulance use. In conclusion, diabetes was a significant risk factor of the severe clinical outcomes especially for in-hospital mortality, mechanical ventilation usage, ICU admission, HD, and CHDF in Japan.

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Individuals living with chronic conditions (CC) typically have a higher risk of more severe outcomes when exposed to infection. Although many studies have investigated the relationship between CCs and COVID-19 severity, they are generally limited to clinical or hospitalized populations. There is a need to estimate the impact of pre-existing CCs on the severity of acute SARS-CoV-2 infection symptoms among the general population.

Data from the Canadian COVID-19 Antibody and Health Survey - Cycle 2, a population-based cross-sectional probability survey across 10 provinces capturing the COVID-19 experiences of respondents from January 2020 to August 2022, were used to assess whether pre-existing CCs increased the odds of more severe self-reported infection symptoms among adults living in Canada. Multivariable regression modelling identified which CCs were independently associated with more severe infection symptoms after adjusting for sex, age at infection, and other significant covariates.

Chronic lung disease (aOR = 1.64, 95% CI: 1.09, 2.46), high blood pressure (aOR = 1.35, 95% CI: 1.13, 1.62), weakened immune system (aOR = 1.46, 95% CI: 1.08, 1.98), chronic fatigue syndrome or fibromyalgia (aOR = 2.20, 95% CI: 1.39, 3.50), and arthritis (aOR = 1.28, 95% CI: 1.04, 1.56) were associated with a higher odds of more severe infection, whereas osteoporosis (aOR = 0.58, 95% CI: 0.39, 0.87) was associated with a lower odds. Limiting modelling to adults with confirmed SARS-CoV-2 infections affected some of the variables retained and adjusted associations.

Our findings contribute to a growing evidence base of associations between pre-existing CCs and adverse outcomes after SARS-CoV-2 infection. Identifying factors associated with more severe infection allows for more targeted prevention strategies and early interventions that can minimize the impact of infection.

Previous studies investigating socioeconomic status and tobacco consumption during the COVID-19 pandemic were survey-based. To extend knowledge beyond prevalence rates and trends of tobacco consumption, qualitative research is needed to identify individual's experiences. There is a critical gap within this context, particularly in low-middle income countries. The aim of the study was to perform a qualitative analysis on consumption patterns of tobacco users from a low-middle income country during the COVID-19 pandemic, and to identify factors influencing motivation to quit tobacco products during the pandemic and the perceptions of self-risk for complications of tobacco consumption and COVID-19.

A qualitative study was conducted in São Paulo, Brazil in September 2020. We used a focus group with semi-structured interviews. Participants were invited to answer questions about behavioral and consumption patterns of tobacco products during early stages of COVID-19 pandemic. Two investigators independently performed triangulation of content of the transcripts. Data were analyzed using inductive content analysis.

Eighteen participants were evaluated (66.7% males) with mean age 34.1 ± 14.9 years. Many participants presented high levels (33.3%) of nicotine dependence. Thematic analysis of participants' narratives resulted in two themes: Theme 1: Behavioral and psychological factors impacting consumption; and Theme 2: Consumption patterns, dependence, and information. Open-coding process resulted on four codes: 1) Behavioral and lifestyle changes; 2) Psychological and motivational factors; 3) Consumption patterns and dependence; and 4) Information exposure and awareness. Nine categories were generated from the codes.

Behavioral and consumption patterns varied significantly in tobacco users in Brazil during the early stages of the COVID-19 pandemic, ranging from increases to no changes. Individuals consuming tobacco products showed awareness about the harmful effects of smoking and COVID-19 complications.

To describe the epidemiology pattern of the COVID-19 pandemic during all Spanish State of Alarm.

Retrospective, observational, cohort and multicenter study. Inclusion criteria: age ≥18 years old, admitted for COVID-19 pneumonia in any of the centers of the HM Hospitals Group. Exclusion criteria: voluntary discharge, death in the emergency department, transfer to centers outside the HM group or incomplete data. State of Alarm period: 31/01/2020 to 05/07/2023. Predominant COVID-19 variant was defined when it exceeded 50% of the total isolates.

During the study period, 2,992 patients were admitted due to a COVID-19 pneumonia, 295 patients (9.86%) non-survive. Survivors and non-survivors were different in age and comorbidities. However, both cohorts presented a similar net of interaction between comorbidities. Hospital admissions per week showed an evolution in "peaks" with "troughs". A total of 197 (6.48%) patients were admitted to the ICU, of whom 52 (26.39%) non-survive; this subgroup stood out for having a higher proportion of septic shock, orotracheal intubation and acute renal failure, as well as a lower proportion of pulmonary thromboembolism and delirium. Concerning the viral variants, the incidence for the original variant was 4.05 cases/day, for the alpha variant 3.82 cases/day, for the delta variant 1.16 cases/day and for the omicron variant 1.35 cases/day.

Almost 1 of 10 patients with COVID-19 pneumonia death, a proportion that increased to 1 of 4 in case of being admitted to the ICU. Unexpectedly, interaction between comorbidities did not differ between survivors and non-survivors patients. Predominant variants were associated with different hospital admission rates but not influence the presence of peak-troughs evolution of the pandemic.

Rationale: Epidemiologic studies on patients with asthma and in vitro data suggest a protective role of type 2 (T2) inflammation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objectives: Using a large, multisite cohort, we studied clinical outcomes after SARS-CoV-2 infection in multiple asthma endotypes and examined the effects of T2-directed biologics in infected patients with asthma. Methods: The National COVID Cohort Collaborative Data Enclave was used to identify and stratify patients with asthma by endotype to include those with non-T2 and T2 asthma, as well as exposure to T2-directed biologic therapy. We evaluated the risk of hospitalization, invasive mechanical ventilation, and 90-day mortality by endotype and exposure to biologics. Results: For this study, 402,376 patients met the inclusion criteria, of whom 138,142 (34%) were characterized as having non-T2 asthma and 264,234 (66%) as having T2 asthma, a group further divided into 104,823 (26%) atopic, 84,440 (21%) eosinophilic, and 74,971 (19%) T2-high asthmatic endotypes. Compared with patients with non-T2 asthma, those with atopic and T2-high asthma experienced decreased odds of hospitalization and 90-day mortality. Conversely, patients with eosinophilic asthma experienced higher odds of hospitalization, intubation, and 90-day mortality. Exposure to T2-directed biologic therapies did not alter outcomes after propensity score matching. In contrast, maximum eosinophil count and recent systemic corticosteroid use were directly correlated with increased odds of all outcomes. Conclusions: Coronavirus disease (COVID-19) outcomes differ depending on asthma endotype, with patients with atopic asthma experiencing lower odds and those with eosinophilic asthma experiencing higher odds of deleterious outcomes. T2-directed biologic treatment did not alter these outcomes, but recent systemic corticosteroid use predisposes all patients with asthma to adverse outcomes.

Obesity is a risk factor for COVID-19 mortality; a BMI >35 increases the risk of death up to 12-fold; two previous studies have examined the association between visceral fat quantified by tomography and the risk of severe COVID-19, but not its association with mortality.

Examine whether tomographic findings differentiated data from patients who died of COVID-19 pneumonia from those who survived in a cohort of patients at a tertiary hospital.

This was a case-control study (1:1) in which we recruited data from patients at a tertiary care hospital in Mexico. Cases (N = 213) were data from patients with COVID-19 pneumonia discharged due to death, and controls (N = 216) were data from patients discharged due to improvement. All had chest computed tomography (CT) scans in the Picture Archiving and Communication System (PACS) platform. Multivariate analysis was used to identify tomographic variables associated with mortality, and odds ratios were calculated. As tomographic variables, we refer to the total severity score, the total percentage of pulmonary involvement, the pattern of involvement, the location of the lesions, and subcutaneous and visceral fat.

A total of 429 sets of data from Mexican patients were analyzed, with an overall age of 57 years (18-93). Sixty-three percent were male, and arterial hypertension was the most common comorbidity in 48.3 %. An odds ratio (OR) of 8.79 (95 % CI 1.44-53.73) was found for visceral fat and mortality; the rest of the tomographic variables did not show a statistically significant association.

Visceral fat was the most significant tomographic risk factor for mortality in patients with COVID-19 pneumonia.

COVID-19 has led to nearly seven million deaths and male sex has been reported as one of the main risk factors for mortality. Few studies have analyzed cohorts of male patients, especially in underrepresented regions in the medical literature, such as low and middle-income nations. To address this gap, we conducted large-scale, male-specific, multinational analyses, to improve understanding of factors associated with mortality in this high-risk population and global variations.

This is a prospective, multicenter study that includes data from the CARDIO COVID-19-20 registry and the WHF COVID-19 CVD study. A multiple Poisson regression model was performed to evaluate differences in factors associated with in-hospital mortality among male COVID-19 patients across different regions.

We analyzed 4,899 hospitalized male COVID-19 patients from 32 countries: Africa (11.2%), the Americas (44.7%), Asia (33.8%), and Europe (10.2%). Median age was 59 years (IQR: 47-69), with 50.5% aged 40-64. ICU admission was 42.4%, and mortality was 19.2%, with marked regional differences (ranging from 6% in Europe to 26.9% in the Americas). Poisson regression showed age >80 years (aRR = 4.21) and IMV (aRR = 3.80) as the strongest factors associated with mortality. Other factors included diabetes, chronic kidney disease, myocarditis, and decompensated heart failure. Mortality risk was higher in Africa (aRR = 3.86), Asia (aRR = 2.72), and the Americas (aRR = 2.23) compared to Europe (p < 0.001). Anticoagulation/Antiplatelet therapy showed a potential correlation with survival.

This study reflects the complexity of factors influencing COVID-19 mortality among male patients hospitalized with COVID-19, emphasizing global variability. The substantial differences in mortality noted across countries are likely due to differences in disease severity, comorbidities, clinical care, and health system factors. Age remains a primary risk factor, with older populations particularly vulnerable. Our findings underscore the need for targeted and tailored regional approaches to manage male COVID-19 patients.

This study evaluates the impact of pre-existing comorbidities and in-hospital complications on COVID-19 mortality rates.

A retrospective single-center study was conducted using electronic health records from 640 COVID-19 patients hospitalized at the University Clinical Centre in Gdansk, Poland, between November 2020 and May 2021. Patients were categorized based on disease severity into stable or ICU wards based on the disease severity. Data on demographics, comorbidities, complications, and treatments were collected and verified. Statistical analyses, including odds ratios (ORs) and confidence intervals (CIs), assessed mortality risk factors supported by python-based processing.

The mean patient age was 67 years (SD ± 15.89), comprising 39% females (n = 250) and 60.94% males (n = 390). Mortality risk was highest in patients aged 65 years and older (OR 3.00; 95% CI, 1.97-4.60). Among the pre-existing comorbidities, chronic kidney disease (OR 3.28; 95% CI, 2.12-5.09), atrial fibrillation (OR 2.43; CI 95%, 1.63-3.61), and heart failure (OR 2.89; 95% CI, 1.91-4.37) were significant predictors of mortality. In hospital complications, such as severe respiratory failure requiring ICU ventilation (OR 23.59; 95% CI, 2.81-197.87), myocardial infarction (OR 25.43; 95% CI, 3.16-204.97), acute kidney injury requiring renal replacement therapy (OR 19.15; 95% CI, 6.49-56.51), sepsis (OR 7.22, 95% CI, 3.77-13.84), stroke, further increased mortality risk.

COVID-19 patients with pre-existing renal and cardiovascular conditions face a higher risk of fatal outcomes. Early diagnosis and intervention targeting these complications are vital to in reducing mortality. Further research is needed to reconcile disparities with existing literature.

Background: Heart failure with preserved ejection fraction (HFpEF) is a condition with limited large-scale data on the short- and long-term effects of SARS-CoV-2 infection. This study aimed to evaluate the prevalence of major adverse cardiac and cerebrovascular events (MACCEs) in HFpEF patients hospitalized with SARS-CoV-2 and identify sex-specific risk factors and predictors of MACCEs in this population. Methods: This retrospective study analyzed HFpEF patients hospitalized with SARS-CoV-2 from the 2020 National Inpatient Sample (NIS) using ICD-10 codes. Patients hospitalized with HFpEF and SARS-CoV-2 were categorized by age (18-44, 45-64, ≥65 years). Multivariate logistic regression was used to adjust for potential confounders, with the statistical significance set at a two-tailed p-value < 0.05. Results: Among 109,750 HFpEF patients hospitalized with SARS-CoV-2, 31,960 (29.1%) experienced MACCEs. Males experienced a higher rate of MACCEs than females (31.1% vs. 27.5%, OR: 1.20, 95% CI: 1.12-1.28, p < 0.001). Adjusted analysis revealed that elderly patients (≥65 years, OR: 1.47, 95% CI: 1.33-1.62) compared with the 45-64 age group and males (OR: 1.20, 95% CI: 1.12-1.28, p < 0.001) had a higher risk of MACCEs. Key predictors included prior coronary artery bypass grafting (CABG; OR: 1.15, 95% CI: 1.02-1.30), cancer (OR: 1.24, 95% CI: 1.08-1.42), and chronic kidney disease (OR: 1.15, 95% CI: 1.08-1.23). Subgroup analysis identified additional sex-specific risk factors. In males, hyperlipidemia, obesity, tobacco use disorder, prior stroke/transient ischemic attack (TIA), prior venous thromboembolism (VTE), alcohol abuse, depression, and valvular disease were significant predictors of MACCEs. In females, hyperlipidemia, tobacco use disorder, prior stroke/TIA, prior VTE, and depression were significant predictors. Conclusions: HFpEF patients hospitalized with SARS-CoV-2 have a high risk of MACCEs, with male sex, older age, prior CABG, cancer, and chronic kidney disease as key risk factors. This study provides the first large-scale analysis of sex-specific predictors of MACCEs in HFpEF patients hospitalized with SARS-CoV-2. These findings underscore the need for focused research and clinical gender-based strategies to mitigate cardiovascular risks in this unique and high-risk population.

Chronic obstructive pulmonary disease (COPD) is a risk factor for severe COVID-19. However, mortality after COVID-19 recovery in this population remains unclear.

We retrospectively enrolled individuals with COPD from the Korean National Health Insurance database. We compared the mortality rate in individuals with COPD who recovered from COVID-19 between 8 October 2020 and 31 December 2021 (COVID-19 cohort, n=2499) with that in 1:1 propensity score-matched controls (n=2499). The study population was followed until either death or 30 September 2022, whichever came first.

The COVID-19 cohort had a 4.8% mortality rate vs 2.7% in matched controls during a median follow-up of 319 days (IQR, 293-422 days), including 14 days of recovery time. The COVID-19 cohort had a higher risk of death than matched controls (adjusted HR (aHR)=1.81, 95% CI=1.35 to 2.45). The risk of mortality was notably higher in individuals with severe COVID-19 (aHR=5.05, 95% CI=3.65 to 6.97), especially during the first 180 days of recovery (highest during the first 30 days (aHR=20.25, 95% CI=7.79 to 52.64)). Non-severe COVID-19 does not increase the risk of mortality compared with controls (aHR=0.85, 95% CI=0.57 to 1.28).

Individuals with COPD recovering from COVID-19 showed an increased risk of long-term mortality, particularly within the first 180 days post-recovery, especially those who experienced severe COVID-19.